Integrating Small Language Models with Retrieval-Augmented Generation in Computing Education: Key Takeaways, Setup, and Practical Insights

Leveraging a Large Language Model (LLM) for personalized learning in computing education is promising, yet cloud-based LLMs pose risks around data security and privacy. To address these concerns, we developed and deployed a locally stored Small Language Model (SLM) utilizing Retrieval-Augmented Generation (RAG) methods to support computing students' learning. Previous work has demonstrated that SLMs can match or surpass popular LLMs (gpt3.5-turbo and gpt-4-32k) in handling conversational data from a CS1 course. We deployed SLMs with RAG (SLM + RAG) in a large course with more than 250 active students, fielding nearly 2,000 student questions, while evaluating data privacy, scalability, and feasibility of local deployments. This paper provides a comprehensive guide for deploying SLM + RAG systems, detailing model selection, vector database choice, embedding methods, and pipeline frameworks. We share practical insights from our deployment, including scalability concerns, accuracy versus context length trade-offs, guardrails and hallucination reduction, as well as data privacy maintenance. We address the "Impossible Triangle" in RAG systems, which states that achieving high accuracy, short context length, and low time consumption simultaneously is not feasible. Furthermore, our novel RAG framework, Intelligence Concentration (IC), categorizes information into multiple layers of abstraction within Milvus collections mitigating trade-offs and enabling educational assistants to deliver more relevant and personalized responses to students quickly.